// Copyright (c) OpenMMLab. All rights reserved. #include "mmdeploy/segmentor.hpp" #include <fstream> #include <numeric> #include <opencv2/imgcodecs/imgcodecs.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <random> #include <string> #include <vector> using namespace std; vector<cv::Vec3b> gen_palette(int num_classes) { std::mt19937 gen; std::uniform_int_distribution<ushort> uniform_dist(0, 255); vector<cv::Vec3b> palette; palette.reserve(num_classes); for (auto i = 0; i < num_classes; ++i) { palette.emplace_back(uniform_dist(gen), uniform_dist(gen), uniform_dist(gen)); } return palette; } int main(int argc, char* argv[]) { if (argc != 4) { fprintf(stderr, "usage:\n image_segmentation device_name model_path image_path\n"); return 1; } auto device_name = argv[1]; auto model_path = argv[2]; auto image_path = argv[3]; cv::Mat img = cv::imread(image_path); if (!img.data) { fprintf(stderr, "failed to load image: %s\n", image_path); return 1; } using namespace mmdeploy; Segmentor segmentor{Model{model_path}, Device{device_name}}; auto result = segmentor.Apply(img); auto palette = gen_palette(result->classes + 1); cv::Mat color_mask = cv::Mat::zeros(result->height, result->width, CV_8UC3); int pos = 0; int total = color_mask.rows * color_mask.cols; std::vector<int> idxs(result->classes); for (auto iter = color_mask.begin<cv::Vec3b>(); iter != color_mask.end<cv::Vec3b>(); ++iter) { // output mask if (result->mask) { *iter = palette[result->mask[pos++]]; } // output score if (result->score) { std::iota(idxs.begin(), idxs.end(), 0); auto k = std::max_element(idxs.begin(), idxs.end(), [&](int i, int j) { return result->score[pos + i * total] < result->score[pos + j * total]; }) - idxs.begin(); *iter = palette[k]; pos += 1; } } img = img * 0.5 + color_mask * 0.5; cv::imwrite("output_segmentation.png", img); return 0; }